Coupled estimation of crop evapotranspiration-yield and assessment of water use efficiency in the North China Plain through a remote sensing-based model

Quantification of crop evapotranspiration (ET) and yield is essential for precision agricultural water management and food security, particularly over long temporal and large regional scales. In this study, we combined a water-carbon coupled model with a GPP-driven crop growth simulation method utilizing remote sensing datasets, to simultaneously estimate ET and yield over the past two decades in the North China Plain. The developed model was tested for two major crops (winter wheat and summer maize) using approximately 20 site-years of observations. For wheat, the root mean square error (RMSE) values of ET and gross primary production (GPP) were 0.57 mm d^-1 and 1.65 gC m^-2 d^-1, and for maize were 0.80 mm d^-1 and 2.92 gC m^-2 d^-1, respectively. Besides, the crop growth simulation agreed well with measurements that R² values were mostly larger than 0.66, and the RMSE of yield was 554.7 for wheat and 1346.6 kg hm^-2 for maize, respectively. The results revealed an increasing trend in the crop water productivity (WP = yield/ET) of wheat, while maize maintained an overall higher WP than wheat during 2001-2018. In addition, the impacts of climate change and human management on the spatiotemporal dynamics of ET-GPP fluxes over the agroecosystems were evaluated. The significantly increased GPP rather than ET dominated the significant increase in water use efficiency (WUE=GPP/ET) in the NCP, accounting for 38.6% of its cropland area. The temporal dynamic of regional mean WUE indicated a significantly increased rate of 0.026 gC kg^-1H₂O per year during 2001-2018. The experimental simulations demonstrated that agricultural management dominated the interannual trend of WUE, with a relative contribution of 79.5%, which was obviously larger than that of atmospheric CO₂ concentration (40.2%) and changes in climate variables (-19.7%). The effects of agricultural management on WUE were further disaggregated across the classified six cropping systems, and 82.4% could be attributed to the management of winter wheat-summer maize rotation system. The remote sensing-based model developed in this study effectively quantifies regional ET and yield for two typical crops, providing critical information for smart agricultural water management. The analysis of agroecosystem WUE under changing environments underscores the dominant role of agricultural management and offers insights for climate adaption in agriculture.